Machine learning challenges in theoretical HEP [PDF]
, 2017 In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH). We start the discussion by defining and then classifying machine learning tasks in theoretical HEP.
Carrazza, Stefano
core +2 more sources
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings [PDF]
International Symposium on Computer Architecture, 2023 In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer for such ML models. Optical circuit switches (OCSes)
N. Jouppi +13 more
semanticscholar +1 more source
Practical Black-Box Attacks against Machine Learning [PDF]
ACM Asia Conference on Computer and Communications Security, 2016 Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious
Nicolas Papernot +5 more
semanticscholar +1 more source
Probability density estimation of photometric redshifts based on machine learning [PDF]
, 2017 Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems.
Amaro, Valeria +5 more
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Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.
PLoS Computational Biology, 2021 Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical ...
Qiang Gu +7 more
doaj +1 more source
PCP-ML: Protein characterization package for machine learning [PDF]
BMC Research Notes, 2014 Machine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction. To speed further development of machine learning based tools and their release to the community, we have developed a package which characterizes several aspects of a protein commonly used for protein prediction tasks with ...
Eickholt, Jesse, Wang, Zheng
openaire +2 more sources
Interactive Automation Of COVID-19 Classification Through X-Ray Images Using Machine Learning
JISR on Computing, 2020 Machine learning had given many benefits to the humankind by implementing technology on the daily human lives. To add, when the pandemic COVID19 hits Earth globally in early 2020, mankind is challenged with the sudden emergence of the virus that costed
Ashura binti Hasmadi +2 more
doaj +1 more source
Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells
Energy & Environmental Materials, 2019 Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning (ML) is a technique for computer to learn how to perform a specific task using known data.
Fan Li +6 more
semanticscholar +1 more source
ML-Plan: Automated machine learning via hierarchical planning [PDF]
Machine Learning, 2018 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mohr, Felix +2 more
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A review of Machine Learning (ML) algorithms used for modeling travel mode choice
DYNA, 2019 In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics.
J. Pineda-Jaramillo
semanticscholar +1 more source